Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Digital pathology for nonalcoholic steatohepatitis assessment

Nature Reviews Gastroenterology & Hepatology volume 21, pages 57–69 (2024)Cite this article

Subjects

Abstract

Histological assessment of nonalcoholic fatty liver disease (NAFLD) has anchored knowledge development about the phenotypes of the condition, their natural history and their clinical course. This fact has led to the use of histological assessment as a reference standard for the evaluation of efficacy of drug interventions for nonalcoholic steatohepatitis (NASH) — the more histologically active form of NAFLD. However, certain limitations of conventional histological assessment systems pose challenges in drug development. These limitations have spurred intense scientific and commercial development of machine learning and digital approaches towards the assessment of liver histology in patients with NAFLD. This research field remains an area in rapid evolution. In this Perspective article, we summarize the current conventional assessment of NASH and its limitations, the use of specific digital approaches for histological assessment, and their application to the study of NASH and its response to therapy. Although this is not a comprehensive review, the leading tools currently used to assess therapeutic efficacy in drug development are specifically discussed. The potential translation of these approaches to support routine clinical assessment of NAFLD and an agenda for future research are also discussed.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Typical histological changes of steatotic liver disease.
Fig. 2: Histological artefacts that might interfere with image analysis.
Fig. 3: Factors affecting the results of studies using digital approaches for pathology assessment of nonalcoholic steatohepatitis.
Fig. 4: Digital pathology platforms and the initial scanning of glass slide images.
Fig. 5: Fibrosis stages highlighted by second harmonic generation technology.
Fig. 6: Analysis of fibrosis regression.

Similar content being viewed by others

References

  1. Rinella, M. E. et al. A multi-society Delphi consensus statement on new fatty liver disease nomenclature. Hepatology https://doi.org/10.1097/HEP.0000000000000520 (2023).

    Article  PubMed  Google Scholar 

  2. Taylor, R. S. et al. Association between fibrosis stage and outcomes of patients with nonalcoholic fatty liver disease: a systematic review and meta-analysis. Gastroenterology 158, 1611–1625.e12 (2020).

    CAS  PubMed  Google Scholar 

  3. Estes, C., Razavi, H., Loomba, R., Younossi, Z. & Sanyal, A. J. Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. Hepatology 67, 123–133 (2018).

    CAS  PubMed  Google Scholar 

  4. Rinella, M. E. et al. AASLD practice guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology 77, 1797–1835 (2023).

    PubMed  Google Scholar 

  5. Brunt, E. M. Nonalcoholic fatty liver disease: pros and cons of histologic systems of evaluation. Int. J. Mol. Sci. 17, 97 (2016).

    PubMed  PubMed Central  Google Scholar 

  6. Younossi, Z. M. et al. Nonalcoholic fatty liver disease: assessment of variability in pathologic interpretations. Mod. Pathol. 11, 560–565 (1998).

    CAS  PubMed  Google Scholar 

  7. Nam, D., Chapiro, J., Paradis, V., Seraphin, T. P. & Kather, J. N. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP Rep. 4, 100443 (2022).

    PubMed  PubMed Central  Google Scholar 

  8. Leevy, C. M., Zinke, M. R., White, T. J. & Gnassi, A. M. Clinical observations on the fatty liver. AMA Arch. Intern. Med. 92, 527–541 (1953).

    CAS  PubMed  Google Scholar 

  9. Thaler, H. Editorial: fatty liver-steatonecrosis-cirrhosis. Acta Hepatogastroenterol. 22, 271–273 (1975).

    CAS  Google Scholar 

  10. Thaler, H. Fatty liver. Tokai J. Exp. Clin. Med. 5, 233–242 (1980).

    CAS  PubMed  Google Scholar 

  11. Dianzani, M. U. On the pathogenesis of the accumulation of fat in hepatic steatosis [Italian]. Rass. Med. Sarda 66, 67–90 (1964).

    CAS  PubMed  Google Scholar 

  12. Popper, H. & Schaffner, F. Editorial: steatosis-mallory’s hyaline-cirrhosis: can their relationships be resolved by an experiment of nature? Gastroenterology 67, 185–188 (1974).

    CAS  PubMed  Google Scholar 

  13. Ludwig, J., Viggiano, T. R., McGill, D. B. & Oh, B. J. Nonalcoholic steatohepatitis: Mayo Clinic experiences with a hitherto unnamed disease. Mayo Clin. Proc. 55, 434–438 (1980).

    CAS  PubMed  Google Scholar 

  14. Brunt, E. M., Janney, C. G., Di Bisceglie, A. M., Neuschwander-Tetri, B. A. & Bacon, B. R. Nonalcoholic steatohepatitis: a proposal for grading and staging the histological lesions. Am. J. Gastroenterol. 94, 2467–2474 (1999).

    CAS  PubMed  Google Scholar 

  15. Kleiner, D. E. et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 41, 1313–1321 (2005).

    PubMed  Google Scholar 

  16. Bedossa, P. & Consortium, F. P. Utility and appropriateness of the fatty liver inhibition of progression (FLIP) algorithm and steatosis, activity, and fibrosis (SAF) score in the evaluation of biopsies of nonalcoholic fatty liver disease. Hepatology 60, 565–575 (2014).

    CAS  PubMed  Google Scholar 

  17. Brunt, E. M. et al. Improvements in histologic features and diagnosis associated with improvement in fibrosis in nonalcoholic steatohepatitis: results from the Nonalcoholic Steatohepatitis Clinical Research Network treatment trials. Hepatology 70, 522–531 (2019).

    CAS  PubMed  Google Scholar 

  18. Desmet, V. J., Gerber, M., Hoofnagle, J. H., Manns, M. & Scheuer, P. J. Classification of chronic hepatitis: diagnosis, grading and staging. Hepatology 19, 1513–1520 (1994).

    CAS  PubMed  Google Scholar 

  19. Kleiner, D. E. et al. Association of histologic disease activity with progression of nonalcoholic fatty liver disease. JAMA Netw. Open. 2, e1912565 (2019).

    PubMed  PubMed Central  Google Scholar 

  20. Matteoni, C. A. et al. Nonalcoholic fatty liver disease: a spectrum of clinical and pathological severity. Gastroenterology 116, 1413–1419 (1999).

    CAS  PubMed  Google Scholar 

  21. Lackner, C. et al. Ballooned hepatocytes in steatohepatitis: the value of keratin immunohistochemistry for diagnosis. J. Hepatol. 48, 821–828 (2008).

    CAS  PubMed  Google Scholar 

  22. Cheung, A. et al. Defining improvement in nonalcoholic steatohepatitis for treatment trial endpoints: recommendations from the liver forum. Hepatology 70, 1841–1855 (2019).

    PubMed  Google Scholar 

  23. Sanyal, A. J. et al. Prospective study of outcomes in adults with nonalcoholic fatty liver disease. N. Engl. J. Med. 385, 1559–1569 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Sanyal, A. J. et al. Tropifexor for nonalcoholic steatohepatitis: an adaptive, randomized, placebo-controlled phase 2a/b trial. Nat. Med. 29, 392–400 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Angulo, P. et al. Liver fibrosis, but no other histologic features, is associated with long-term outcomes of patients with nonalcoholic fatty liver disease. Gastroenterology 149, 389–397.e10 (2015).

    PubMed  Google Scholar 

  26. Ekstedt, M. et al. Long-term follow-up of patients with NAFLD and elevated liver enzymes. Hepatology 44, 865–873 (2006).

    CAS  PubMed  Google Scholar 

  27. Hagstrom, H. et al. Fibrosis stage but not NASH predicts mortality and time to development of severe liver disease in biopsy-proven NAFLD. J. Hepatol. 67, 1265–1273 (2017).

    PubMed  Google Scholar 

  28. Brunt, E. M. et al. Misuse of scoring systems. Hepatology 54, 369–370; author reply 370–371 (2011).

  29. Naoumov, N. V. et al. Digital pathology with artificial intelligence analyses provides greater insights into treatment-induced fibrosis regression in NASH. J. Hepatol. 77, 1399–1409 (2022).

    CAS  PubMed  Google Scholar 

  30. Popa, S. L. et al. Non-alcoholic fatty liver disease: implementing complete automated diagnosis and staging. a systematic review. Diagnostics 11, 1078 (2021).

    PubMed  PubMed Central  Google Scholar 

  31. Teramoto, T., Shinohara, T. & Takiyama, A. Computer-aided classification of hepatocellular ballooning in liver biopsies from patients with NASH using persistent homology. Comput. Methods Prog. Biomed. 195, 105614 (2020).

    Google Scholar 

  32. Brunt, E. M. et al. Complexity of ballooned hepatocyte feature recognition: defining a training atlas for artificial intelligence-based imaging in NAFLD. J. Hepatol. 76, 1030–1041 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Rockey, D. C. et al. Liver biopsy. Hepatology 49, 1017–1044 (2009).

    PubMed  Google Scholar 

  34. Arun, J., Jhala, N., Lazenby, A. J., Clements, R. & Abrams, G. A. Influence of liver biopsy heterogeneity and diagnosis of nonalcoholic steatohepatitis in subjects undergoing gastric bypass. Obes. Surg. 17, 155–161 (2007).

    PubMed  Google Scholar 

  35. Arun, J., Clements, R. H., Lazenby, A. J., Leeth, R. R. & Abrams, G. A. The prevalence of nonalcoholic steatohepatitis is greater in morbidly obese men compared to women. Obes. Surg. 16, 1351–1358 (2006).

    PubMed  Google Scholar 

  36. Ratziu, V. et al. Sampling variability of liver biopsy in nonalcoholic fatty liver disease. Gastroenterology 128, 1898–1906 (2005).

    PubMed  Google Scholar 

  37. van Seijen, M. et al. Impact of delayed and prolonged fixation on the evaluation of immunohistochemical staining on lung carcinoma resection specimen. Virchows Arch. 475, 191–199 (2019).

    PubMed  PubMed Central  Google Scholar 

  38. Taqi, S. A., Sami, S. A., Sami, L. B. & Zaki, S. A. A review of artifacts in histopathology. J. Oral. Maxillofac. Pathol. 22, 279 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. Farrell, D. J., Thompson, P. J. & Morley, A. R. Tissue artefacts caused by sponges. J. Clin. Pathol. 45, 923–924 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Vahadane, A. et al. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35, 1962–1971 (2016).

    PubMed  Google Scholar 

  41. Guy, C. D. et al. Costaining for keratins 8/18 plus ubiquitin improves detection of hepatocyte injury in nonalcoholic fatty liver disease. Hum. Pathol. 43, 790–800 (2012).

    CAS  PubMed  Google Scholar 

  42. Zipfel, W. R. et al. Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation. Proc. Natl Acad. Sci. USA 100, 7075–7080 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Pirhonen, J. et al. Continuous grading of early fibrosis in NAFLD using label-free imaging: a proof-of-concept study. PLoS ONE 11, e0147804 (2016).

    PubMed  PubMed Central  Google Scholar 

  44. Wang, Y. et al. Dual-photon microscopy-based quantitation of fibrosis-related parameters (q-FP) to model disease progression in steatohepatitis. Hepatology 65, 1891–1903 (2017).

    CAS  PubMed  Google Scholar 

  45. Goodman, Z. D., Becker, R. L. Jr., Pockros, P. J. & Afdhal, N. H. Progression of fibrosis in advanced chronic hepatitis C: evaluation by morphometric image analysis. Hepatology 45, 886–894 (2007).

    CAS  PubMed  Google Scholar 

  46. Patel, A. et al. Contemporary whole slide imaging devices and their applications within the modern pathology department: a selected hardware review. J. Pathol. Inf. 12, 50 (2021).

    Google Scholar 

  47. FDA. Biomarker Qualification: Evidentiary Framework Guidance for Industry and FDA Staff (Draft Guidance) (US Federal Govt., 2018).

  48. Sun, W. et al. Nonlinear optical microscopy: use of second harmonic generation and two-photon microscopy for automated quantitative liver fibrosis studies. J. Biomed. Opt. 13, 064010 (2008).

    PubMed  Google Scholar 

  49. Campagnola, P. Second harmonic generation imaging microscopy: applications to diseases diagnostics. Anal. Chem. 83, 3224–3231 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Guilbert, T. et al. A robust collagen scoring method for human liver fibrosis by second harmonic microscopy. Opt. Express 18, 25794–25807 (2010).

    CAS  PubMed  Google Scholar 

  51. Gailhouste, L. et al. Fibrillar collagen scoring by second harmonic microscopy: a new tool in the assessment of liver fibrosis. J. Hepatol. 52, 398–406 (2010).

    CAS  PubMed  Google Scholar 

  52. Tai, D. C. et al. Fibro-C-Index: comprehensive, morphology-based quantification of liver fibrosis using second harmonic generation and two-photon microscopy. J. Biomed. Opt. 14, 044013 (2009).

    PubMed  Google Scholar 

  53. Guy, C. D. et al. Hedgehog pathway activation parallels histologic severity of injury and fibrosis in human nonalcoholic fatty liver disease. Hepatology 55, 1711–1721 (2012).

    CAS  PubMed  Google Scholar 

  54. Saldarriaga, O. A. et al. Multispectral imaging enables characterization of intrahepatic macrophages in patients with chronic liver disease. Hepatol. Commun. 4, 708–723 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Traum, D. et al. Highly multiplexed 2-dimensional imaging mass cytometry analysis of HBV-infected liver. JCI Insight 6, e146883 (2021).

    PubMed  PubMed Central  Google Scholar 

  56. Hanna, M. G. et al. Validation of a digital pathology system including remote review during the COVID-19 pandemic. Mod. Pathol. 33, 2115–2127 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Jahn, S. W., Plass, M. & Moinfar, F. Digital pathology: advantages, limitations and emerging perspectives. J. Clin. Med. 9, 3697 (2020).

    PubMed  PubMed Central  Google Scholar 

  58. Petersen, K. F., West, A. B., Reuben, A., Rothman, D. L. & Shulman, G. I. Noninvasive assessment of hepatic triglyceride content in humans with 13C nuclear magnetic resonance spectroscopy. Hepatology 24, 114–117 (1996).

    CAS  PubMed  Google Scholar 

  59. Turlin, B. et al. Assessment of hepatic steatosis: comparison of quantitative and semiquantitative methods in 108 liver biopsies. Liver Int. 29, 530–535 (2009).

    PubMed  Google Scholar 

  60. Marti-Aguado, D. et al. Digital pathology enables automated and quantitative assessment of inflammatory activity in patients with chronic liver disease. Biomolecules 11, 1808 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Heinemann, F., Birk, G. & Stierstorfer, B. Deep learning enables pathologist-like scoring of NASH models. Sci. Rep. 9, 18454 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Zeng, C. et al. Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning. J. Pathol. 252, 53–64 (2020).

    PubMed  Google Scholar 

  63. Taylor-Weiner, A. et al. A machine learning approach enables quantitative measurement of liver histology and disease monitoring in NASH. Hepatology 74, 133–147 (2021).

    PubMed  Google Scholar 

  64. Moraru, L. et al. Texture analysis of parasitological liver fibrosis images. Microsc. Res. Tech. 80, 862–869 (2017).

    CAS  PubMed  Google Scholar 

  65. Xu, S. et al. qFibrosis: a fully-quantitative innovative method incorporating histological features to facilitate accurate fibrosis scoring in animal model and chronic hepatitis B patients. J. Hepatol. 61, 260–269 (2014).

    PubMed  PubMed Central  Google Scholar 

  66. Qu, H. et al. Training of computational algorithms to predict NAFLD activity score and fibrosis stage from liver histopathology slides. Comput. Methods Prog. Biomed. 207, 106153 (2021).

    Google Scholar 

  67. Vanderbeck, S. et al. Automatic quantification of lobular inflammation and hepatocyte ballooning in nonalcoholic fatty liver disease liver biopsies. Hum. Pathol. 46, 767–775 (2015).

    PubMed  PubMed Central  Google Scholar 

  68. Vanderbeck, S., Bockhorst, J., Komorowski, R., Kleiner, D. E. & Gawrieh, S. Automatic classification of white regions in liver biopsies by supervised machine learning. Hum. Pathol. 45, 785–792 (2014).

    PubMed  Google Scholar 

  69. Liu, F. et al. qFIBS: an automated technique for quantitative evaluation of fibrosis, inflammation, ballooning, and steatosis in patients with nonalcoholic steatohepatitis. Hepatology 71, 1953–1966 (2020).

    CAS  PubMed  Google Scholar 

  70. Hernest, M. et al. New approach of fibrosis by multiphoton microscopy with second harmonic generation [French]. Med. Sci. 22, 820–821 (2006).

    Google Scholar 

  71. Wang, T. H., Chen, T. C., Teng, X., Liang, K. H. & Yeh, C. T. Automated biphasic morphological assessment of hepatitis B-related liver fibrosis using second harmonic generation microscopy. Sci. Rep. 5, 12962 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Wang, Y. et al. Dual photon microscopy based quantitation of fibrosis-related parameters (q-FP) to model disease progression in steatohepatitis: methodological issues. Hepatology 66, 998–999 (2017).

    Google Scholar 

  73. Chang, P. E. et al. Second harmonic generation microscopy provides accurate automated staging of liver fibrosis in patients with non-alcoholic fatty liver disease. PLoS ONE 13, e0199166 (2018).

    PubMed  PubMed Central  Google Scholar 

  74. Kvilekval, K., Fedorov, D., Obara, B., Singh, A. & Manjunath, B. S. Bisque: a platform for bioimage analysis and management. Bioinformatics 26, 544–552 (2010).

    CAS  PubMed  Google Scholar 

  75. Friedman, S. L., Neuschwander-Tetri, B. A., Rinella, M. & Sanyal, A. J. Mechanisms of NAFLD development and therapeutic strategies. Nat. Med. 24, 908–922 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. FDA. Noncirrhotic Nonalcoholic Steatohepatitis with Liver Fibrosis: Developing Drugs for Treatment Guidance for Industry (FDA, 2018).

  77. Romeo, S., Sanyal, A. & Valenti, L. Leveraging human genetics to identify potential new treatments for fatty liver disease. Cell Metab. 31, 35–45 (2020).

    CAS  PubMed  Google Scholar 

  78. Siddiqui, M. S. et al. Severity of nonalcoholic fatty liver disease and progression to cirrhosis are associated with atherogenic lipoprotein profile. Clin. Gastroenterol. Hepatol. 13, 1000–1008.e3 (2015).

    CAS  PubMed  Google Scholar 

  79. Tamaki, N. et al. Clinical utility of 30% relative decline in MRI-PDFF in predicting fibrosis regression in non-alcoholic fatty liver disease. Gut 71, 983–990 (2021).

    PubMed  Google Scholar 

  80. Loomba, R. et al. Multicenter validation of association between decline in MRI-PDFF and histologic response in NASH. Hepatology 72, 1219–1229 (2020).

    CAS  PubMed  Google Scholar 

  81. Patel, J. et al. Association of noninvasive quantitative decline in liver fat content on MRI with histologic response in nonalcoholic steatohepatitis. Ther. Adv. Gastroenterol. 9, 692–701 (2016).

    CAS  Google Scholar 

  82. Stine, J. G. et al. Change in MRI-PDFF and histologic response in patients with nonalcoholic steatohepatitis: a systematic review and meta-analysis. Clin. Gastroenterol. Hepatol. 19, 2274–2283.e5 (2021).

    PubMed  Google Scholar 

  83. Newsome, P. N. et al. A placebo-controlled trial of subcutaneous semaglutide in nonalcoholic steatohepatitis. N. Engl. J. Med. 384, 1113–1124 (2021).

    CAS  PubMed  Google Scholar 

  84. Sanyal, A. J. et al. Pioglitazone, vitamin E, or placebo for nonalcoholic steatohepatitis. N. Engl. J. Med. 362, 1675–1685 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Roy, M. et al. Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies. Lab. Invest. 100, 1367–1383 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Levene, A. P. et al. Quantifying hepatic steatosis — more than meets the eye. Histopathology 60, 971–981 (2012).

    PubMed  Google Scholar 

  87. Li, M. et al. Comparing morphometric, biochemical, and visual measurements of macrovesicular steatosis of liver. Hum. Pathol. 42, 356–360 (2011).

    PubMed  Google Scholar 

  88. Lee, M. J. et al. Liver steatosis assessment: correlations among pathology, radiology, clinical data and automated image analysis software. Pathol. Res. Pract. 209, 371–379 (2013).

    PubMed  Google Scholar 

  89. Brunt, E. M. Nonalcoholic steatohepatitis: definition and pathology. Semin. Liver Dis. 21, 3–16 (2001).

    CAS  PubMed  Google Scholar 

  90. Forlano, R. et al. High-throughput, machine learning-based quantification of steatosis, inflammation, ballooning, and fibrosis in biopsies from patients with nonalcoholic fatty liver disease. Clin. Gastroenterol. Hepatol. 18, 2081–2090.e9 (2020).

    PubMed  PubMed Central  Google Scholar 

  91. Pai, R. K. et al. Reliability of histologic assessment for NAFLD and development of an expanded NAFLD activity score. Hepatology 76, 1150–1163 (2022).

    CAS  PubMed  Google Scholar 

  92. Gill, R. M. et al. The nonalcoholic steatohepatitis extended hepatocyte ballooning score: histologic classification and clinical significance. Hepatol. Commun. 7, e0033 (2023).

    PubMed  PubMed Central  Google Scholar 

  93. Kleiner, D. E. & Brunt, E. M. Nonalcoholic fatty liver disease: pathologic patterns and biopsy evaluation in clinical research. Semin. Liver Dis. 32, 3–13 (2012).

    CAS  PubMed  Google Scholar 

  94. Neuschwander-Tetri, B. A. et al. Clinical, laboratory and histological associations in adults with nonalcoholic fatty liver disease. Hepatology 52, 913–924 (2010).

    CAS  PubMed  Google Scholar 

  95. Brunt, E. M. et al. Nonalcoholic fatty liver disease (NAFLD) activity score and the histopathologic diagnosis in NAFLD: distinct clinicopathologic meanings. Hepatology 53, 810–820 (2011).

    CAS  PubMed  Google Scholar 

  96. Lefkowitch, J. H., Haythe, J. H. & Regent, N. Kupffer cell aggregation and perivenular distribution in steatohepatitis. Mod. Pathol. 15, 699–704 (2002).

    PubMed  Google Scholar 

  97. Brunt, E. M. et al. Portal chronic inflammation in nonalcoholic fatty liver disease (NAFLD): a histologic marker of advanced NAFLD — clinicopathologic correlations from the Nonalcoholic Steatohepatitis Clinical Research Network. Hepatology 49, 809–820 (2009).

    PubMed  Google Scholar 

  98. Gadd, V. L. et al. The portal inflammatory infiltrate and ductular reaction in human nonalcoholic fatty liver disease. Hepatology 59, 1393–1405 (2014).

    PubMed  Google Scholar 

  99. Ghany, M. G. et al. Progression of fibrosis in chronic hepatitis C. Gastroenterology 124, 97–104 (2003).

    PubMed  Google Scholar 

  100. Dhingra, S., Mahadik, J. D., Tarabishy, Y., May, S. B. & Vierling, J. M. Prevalence and clinical significance of portal inflammation, portal plasma cells, interface hepatitis and biliary injury in liver biopsies from patients with non-alcoholic steatohepatitis. Pathology 54, 686–693 (2022).

    CAS  PubMed  Google Scholar 

  101. Kleiner, D. E. et al. Hepatic pathology among patients without known liver disease undergoing bariatric surgery: observations and a perspective from the longitudinal assessment of bariatric surgery (LABS) study. Semin. Liver Dis. 34, 98–107 (2014).

    PubMed  PubMed Central  Google Scholar 

  102. Ramachandran, P. et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575, 512–518 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Mirshahi, F. et al. Distinct hepatic immunological patterns are associated with the progression or inhibition of hepatocellular carcinoma. Cell Rep. 38, 110454 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Koh, T. J. & DiPietro, L. A. Inflammation and wound healing: the role of the macrophage. Expert Rev. Mol. Med. 13, e23 (2011).

    PubMed  PubMed Central  Google Scholar 

  105. Millian, D. E. et al. Cutting-edge platforms for analysis of immune cells in the hepatic microenvironment-focus on tumor-associated macrophages in hepatocellular carcinoma. Cancers 14, 1861 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Altamirano, J. et al. A histologic scoring system for prognosis of patients with alcoholic hepatitis. Gastroenterology 146, 1231–1239.e1-6 (2014).

    PubMed  Google Scholar 

  107. Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).

    CAS  PubMed  Google Scholar 

  108. Sanyal, A. J. et al. Cirrhosis regression is associated with improved clinical outcomes in patients with nonalcoholic steatohepatitis. Hepatology 75, 1235–1246 (2021).

    Google Scholar 

  109. Sandrini, J. et al. Quantification of portal-bridging fibrosis area more accurately reflects fibrosis stage and liver stiffness than whole fibrosis or perisinusoidal fibrosis areas in chronic hepatitis C. Mod. Pathol. 27, 1035–1045 (2014).

    CAS  PubMed  Google Scholar 

  110. Calvaruso, V. et al. Computer-assisted image analysis of liver collagen: relationship to Ishak scoring and hepatic venous pressure gradient. Hepatology 49, 1236–1244 (2009).

    PubMed  Google Scholar 

  111. Hall, A. R., Tsochatzis, E., Morris, R., Burroughs, A. K. & Dhillon, A. P. Sample size requirement for digital image analysis of collagen proportionate area in cirrhotic livers. Histopathology 62, 421–430 (2013).

    PubMed  Google Scholar 

  112. Bedossa, P., Dargere, D. & Paradis, V. Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology 38, 1449–1457 (2003).

    PubMed  Google Scholar 

  113. Mostaco-Guidolin, L. B. et al. Collagen morphology and texture analysis: from statistics to classification. Sci. Rep. 3, 2190 (2013).

    PubMed  PubMed Central  Google Scholar 

  114. Gawrieh, S. et al. Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD. Ann. Diagnostic Pathol. 47, 151518 (2020).

    Google Scholar 

  115. Leow, W. Q. et al. An improved qFibrosis algorithm for precise screening and enrollment into non-alcoholic steatohepatitis (NASH) clinical trials. Diagnostics 10, 643 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Sun, Y. et al. New classification of liver biopsy assessment for fibrosis in chronic hepatitis B patients before and after treatment. Hepatology 65, 1438–1450 (2017).

    CAS  PubMed  Google Scholar 

  117. Wanless, I. R., Nakashima, E. & Sherman, M. Regression of human cirrhosis. Morphologic features and the genesis of incomplete septal cirrhosis. Arch. Pathol. Lab. Med. 124, 1599–1607 (2000).

    CAS  PubMed  Google Scholar 

  118. Hytiroglou, P. & Theise, N. D. Regression of human cirrhosis: an update, 18 years after the pioneering article by Wanless et al. Virchows Arch. 473, 15–22 (2018).

    PubMed  Google Scholar 

  119. Ng, N. et al. Second-harmonic generated quantifiable fibrosis parameters provide signatures for disease progression and regression in nonalcoholic fatty liver disease. Clin. Pathol. 16, 2632010X231162317 (2023).

    PubMed  PubMed Central  Google Scholar 

  120. Soon, G. S. T. et al. Artificial intelligence improves pathologist agreement for fibrosis scores in nonalcoholic steatohepatitis patients. Clin. Gastroenterol. Hepatol. 21, 1940–1942.e3 (2022).

    PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank the VCU Stravitz‐Sanyal Institute for Liver Disease and Metabolic Health, the RO1 DK129564 and the Intramural Research Program of the National Institutes of Health, National Cancer Institute.

Author information

Authors and Affiliations

  1. Stravitz‐Sanyal Institute for Liver Disease and Metabolic Health, Virginia Commonwealth University School of Medicine, Richmond, VA, USA

    Arun J. Sanyal

  2. Food and Drug Administration, Silver Spring, MD, USA

    Prakash Jha

  3. Post‐Mortem Section Laboratory of Pathology Center for Cancer Research National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    David E. Kleiner

Authors
  1. Arun J. Sanyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prakash Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David E. Kleiner
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors researched data for the article. All authors contributed substantially to discussion of the content. A.J.S. wrote the article and reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Arun J. Sanyal.

Ethics declarations

Competing interests

A.J.S. has served as a consultant to Path‐AI, HistoIndex, Fibronest, Biocellvia, Merck, Pfizer, Eli Lilly, Novo Nordisk, Boehringer Ingelheim, AstraZeneca, Akero, Intercept, Madrigal, Northsea, Takeda, Regeneron, Genentech, Alnylam, Roche, GlaxoSmithKline, Novartis, Tern, Fractyl, Inventiva, Gilead and Target Pharmasolutions, has stock options in Genfit, Tiziana, Durect, Inversago and Hemoshear, and receives royalties from Uptodate and Elsevier. His institution has received grants from Intercept, Pfizer, Merck, Bristol Myers Squibb, Eli Lilly, Novo Nordisk, Boehringer Ingelheim, AstraZeneca, Novartis and Madrigal. Virginia Commonwealth Univerisity has a collaborative agreement with Avant Sante. D.E.K. has uncompensated collaborative projects with HistoIndex and HighTide. P.J. declares no competing interests.

Peer review

Peer review information

Nature Reviews Gastroenterology & Hepatology thanks Michael Pavlides and Olivier Govaere for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sanyal, A.J., Jha, P. & Kleiner, D.E. Digital pathology for nonalcoholic steatohepatitis assessment. Nat Rev Gastroenterol Hepatol 21, 57–69 (2024). https://doi.org/10.1038/s41575-023-00843-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41575-023-00843-7

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research